Contrast Pattern Mining and Its Application for Building Robust Classifiers

2010 ◽  
pp. 33-33 ◽  
Author(s):  
Kotagiri Ramamohanarao
Keyword(s):  
Pattern Mining ◽  
Contrast Pattern

Contrast Pattern Mining in Folk Music Analysis

2015 ◽  
pp. 393-424 ◽  
Author(s):  
Kerstin Neubarth ◽  
Darrell Conklin
Keyword(s):  
Folk Music ◽  
Pattern Mining ◽  
Music Analysis ◽  
Contrast Pattern

scholarly journals Mining Conditional Contrast Patterns

2009 ◽  
pp. 294-310 ◽  
Author(s):  
Guozhu Dong ◽  
Jinyan Li ◽  
Guimei Liu ◽  
Limsoon Wong
Keyword(s):  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Concise Representation ◽  
Contrast Pattern ◽  
The Difference ◽  
Closed Pattern ◽  
Small Change ◽  
Minimal Generators ◽  
Theoretical Results

This chapter considers the problem of “conditional contrast pattern mining.” It is related to contrast mining, where one considers the mining of patterns/models that contrast two or more datasets, classes, conditions, time periods, and so forth. Roughly speaking, conditional contrasts capture situations where a small change in patterns is associated with a big change in the matching data of the patterns. More precisely, a conditional contrast is a triple (B, F1, F2) of three patterns; B is the condition/context pattern of the conditional contrast, and F1 and F2 are the contrasting factors of the conditional contrast. Such a conditional contrast is of interest if the difference between F1 and F2 as itemsets is relatively small, and the difference between the corresponding matching dataset of B?F1 and that of B?F2 is relatively large. It offers insights on “discriminating” patterns for a given condition B. Conditional contrast mining is related to frequent pattern mining and analysis in general, and to the mining and analysis of closed pattern and minimal generators in particular. It can also be viewed as a new direction for the analysis (and mining) of frequent patterns. After formalizing the concepts of conditional contrast, the chapter will provide some theoretical results on conditional contrast mining. These results (i) relate conditional contrasts with closed patterns and their minimal generators, (ii) provide a concise representation for conditional contrasts, and (iii) establish a so-called dominance-beam property. An efficient algorithm will be proposed based on these results, and experiment results will be reported. Related works will also be discussed.

Scalable Contrast Pattern Mining over Data Streams

2021 ◽  
Author(s):  
Elaheh Alipourchavary ◽  
Sarah M. Erfani ◽  
Christopher Leckie
Keyword(s):  
Data Streams ◽  
Pattern Mining ◽  
Contrast Pattern

An Adaptive Data Distribution Through Tree Rules in Frequent Pattern Mining

2018 ◽  
pp. 300-305
Keyword(s):  
Information Sharing ◽  
Pattern Mining ◽  
Data Distribution ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
General Development ◽  
Secure Information ◽  
Evaluation Parameters ◽  
Secure Information Sharing

Information sharing among the associations is a general development in a couple of zones like business headway and exhibiting. As bit of the touchy principles that ought to be kept private may be uncovered and such disclosure of delicate examples may impacts the advantages of the association that have the data. Subsequently the standards which are delicate must be secured before sharing the data. In this paper to give secure information sharing delicate guidelines are bothered first which was found by incessant example tree. Here touchy arrangement of principles are bothered by substitution. This kind of substitution diminishes the hazard and increment the utility of the dataset when contrasted with different techniques. Examination is done on certifiable dataset. Results shows that proposed work is better as appear differently in relation to various past strategies on the introduce of evaluation parameters.

924-P: Pattern Mining of Trajectories of Glucose Values of Continuous Glucose Monitoring System by Artificial Intelligence in Type 2 Diabetes Patients

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 924-P
Author(s):  
MASAKI MAKINO ◽  
RYO YOSHIMOTO ◽  
MIZUHO KONDO-ANDO ◽  
YASUMASA YOSHINO ◽  
IZUMI HIRATSUKA ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Type 2 Diabetes ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Pattern Mining ◽  
Glucose Monitoring ◽  
Continuous Glucose Monitoring System ◽  
Diabetes Patients

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

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